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Conversational Response Selection

Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–10 of 46 papers

Title	Date	Tasks	Status	Hype
Efficient Dynamic Hard Negative Sampling for Dialogue Selection	Aug 16, 2024	Conversational Response Selection	CodeCode Available	0
P5: Plug-and-Play Persona Prompting for Personalized Response Selection	Oct 10, 2023	ChatbotConversational Response Selection	CodeCode Available	0
Knowledge-aware response selection with semantics underlying multi-turn open-domain conversations	Jul 27, 2023	Conversational Response Selection	CodeCode Available	0
Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems	Jun 7, 2023	Conversational Response SelectionDecoder	CodeCode Available	0
Learning Dialogue Representations from Consecutive Utterances	May 26, 2022	Contrastive LearningConversational Question Answering	CodeCode Available	1
One Agent To Rule Them All: Towards Multi-agent Conversational AI	Mar 15, 2022	AllConversational Response Selection	CodeCode Available	0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue	Mar 1, 2022	Contrastive LearningConversational Response Selection	—Unverified	0
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning	Nov 19, 2021	Contrastive LearningConversational Response Selection	—Unverified	0
Exploring Dense Retrieval for Dialogue Response Selection	Oct 13, 2021	Conversational Response SelectionRetrieval	CodeCode Available	1
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues	Aug 17, 2021	Conversational Response SelectionRetrieval	CodeCode Available	0

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All datasets Ubuntu Dialogue (v1, Ranking)Douban E-commerce RRS DSTC7 Ubuntu PolyAI Reddit Ubuntu IRC RRS Ranking Test Persona-Chat PolyAI AmazonQA Advising Corpus personachat

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Dial-MAE	R10@1	0.92	—	Unverified
2	BERT-FP+EDHNS	R10@1	0.92	—	Unverified
3	Uni-Enc+BERT-FP	R10@1	0.92	—	Unverified
4	BERT-FP	R10@1	0.91	—	Unverified
5	BERT-UMS+FGC	R10@1	0.89	—	Unverified
6	Uni-Encoder	R10@1	0.89	—	Unverified
7	BERT-SL	R10@1	0.88	—	Unverified
8	Poly-encoder	R10@1	0.88	—	Unverified
9	UMS_BERT+	R10@1	0.88	—	Unverified
10	BERT-VFT	R10@1	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SEMSOL(W/o utterances)	MAP	0.65	—	Unverified
2	Uni-Enc+BERT-FP	MAP	0.65	—	Unverified
3	BERT-FP	MAP	0.64	—	Unverified
4	SEMSOL	MAP	0.64	—	Unverified
5	SA-BERT+HCL	MAP	0.64	—	Unverified
6	UMS_BERT+	MAP	0.63	—	Unverified
7	Uni-Encoder	MAP	0.62	—	Unverified
8	SA-BERT	MAP	0.62	—	Unverified
9	Poly-encoder	MAP	0.61	—	Unverified
10	BERT	MAP	0.59	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT-FP+EDHNS	R10@1	0.96	—	Unverified
2	DialMAE	R10@1	0.93	—	Unverified
3	BERT-TL	R10@1	0.93	—	Unverified
4	BERT-FP	R10@1	0.87	—	Unverified
5	BERT-SL	R10@1	0.78	—	Unverified
6	UMS_BERT+	R10@1	0.76	—	Unverified
7	SA-BERT+HCL	R10@1	0.72	—	Unverified
8	SA-BERT	R10@1	0.7	—	Unverified
9	IMN	R10@1	0.62	—	Unverified
10	U2U-IMN	R10@1	0.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT-FP	MAP	0.7	—	Unverified
2	SA-BERT+BERT-FP	MAP	0.7	—	Unverified
3	SA-BERT+HCL	MAP	0.67	—	Unverified
4	BERT	MAP	0.63	—	Unverified
5	MSN	MAP	0.55	—	Unverified
6	DAM	MAP	0.51	—	Unverified
7	SMN	MAP	0.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Multi-context ConveRT	1-of-100 Accuracy	71.2	—	Unverified
2	Bi-encoder (v2)	1-of-100 Accuracy	70.9	—	Unverified
3	Bi-encoder	1-of-100 Accuracy	66.3	—	Unverified
4	Sequential Attention-based Network	1-of-100 Accuracy	64.5	—	Unverified
5	Sequential Inference Models	1-of-100 Accuracy	60.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Multi-context ConveRT	1-of-100 Accuracy	71.8	—	Unverified
2	ConveRT	1-of-100 Accuracy	68.3	—	Unverified
3	PolyAI Encoder	1-of-100 Accuracy	61.3	—	Unverified
4	USE	1-of-100 Accuracy	47.7	—	Unverified
5	ELMO	1-of-100 Accuracy	19.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MPC-BERT	Accuracy	63.64	—	Unverified
2	SA-BERT	Accuracy	60.42	—	Unverified
3	SIRNN	Accuracy	40.83	—	Unverified
4	Dynamic Modeling (n=10)	Accuracy	36.93	—	Unverified
5	DRNN	Accuracy	36.93	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Poly-encoder	NDCG@3	0.68	—	Unverified
2	SA-BERT+BERT-FP	NDCG@3	0.67	—	Unverified
3	BERT	NDCG@3	0.63	—	Unverified
4	BERT-FP	NDCG@3	0.61	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Uni-Encoder	MRR	0.92	—	Unverified
2	P5	R20@1	0.88	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ConveRT	1-of-100 Accuracy	84.3	—	Unverified
2	PolyAI Encoder	1-of-100 Accuracy	71.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CtxDec & -Rev	R@1	31	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	P5	R20@1	87.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PolyAI Encoder	1-of-100 Accuracy	30.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Uni-Encoder	R10@1	0.86	—	Unverified